{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "import os\n",
    "import shapely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "shp_file = gpd.read_file(r'./输入数据/矢量样本数据/SENLIN.shp')#读取shp文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>class</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((674331.228 3428475.071, 674404.041 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((674524.268 3428465.757, 674658.042 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((674751.175 3427847.689, 674927.282 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((674866.830 3428952.761, 675009.071 3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((673140.050 3429268.992, 673224.717 3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  class                                           geometry\n",
       "0   0      5  POLYGON ((674331.228 3428475.071, 674404.041 3...\n",
       "1   0      5  POLYGON ((674524.268 3428465.757, 674658.042 3...\n",
       "2   0      5  POLYGON ((674751.175 3427847.689, 674927.282 3...\n",
       "3   0      5  POLYGON ((674866.830 3428952.761, 675009.071 3...\n",
       "4   0      5  POLYGON ((673140.050 3429268.992, 673224.717 3..."
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp_file.head()#查看shp文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5YNEq7lu4kr+s3ZyX53ht3Ra+flstt08+oVUd+r7PwJA0DvgHYCbwFLAVGARcJWkN8IOIaN6VYcyKxB9ffZsbZ7/KvGXvpDpNYHPVrHiXS+59jusmDUf7OOVgITUYGJIGAZ8DJu0RCq8AjyRbHd8Crstrh2atxBF9u7C1bmdBwmK33z37BoN6deTCU4YW7kn3ocEjPSPirxHxj7vDQtKoPR7/Y0Rc19DyknpImiipdU73mjVSj44V3DH5RE45oqqgz3vdrKU8sGj/5zIthMYcGv4NSfMkXSbp4H0NlNQXeAgYBcyWVJnUqyQtSm53lfSwpJmS7pdUIam7pD9IelLSz7LW9wtJT0v6fmNrZrnUvqKMn331OL5yQmGPlfjHe56jZnnLTxumDoyIOA8YD7xMJgQel/TJBoYfCVwUEdOAR4Hjkvo1ZCZOAc4Ero2IicBq4FTgLOD2iBgHdJZULekMoCwixgD9JA1JW0v/NpilV17WhmlfOIrvTizcbsL2nbs4/7ZaVryTn0nWtFIHhqQTgB8DlwN3AxcD0+obGxGzImK+pPFktjLmSToZ2EwmHIiImyJiZrJIJbAGeAcYJqkbcAjwGjABuCsZ9wQwthE1s7yQxLc+MYR//eIxBTvL17rN2zn3lgVsyNHHt03RmF2SKcADwKiI+F5ELAIuaWiwMtO6k4A6QMAVwKX1jBsNdI+I+WQ+iRkCfBtYArwLdAR278BtBKoaUauvr/Ml1UiqWbu2NK+Pabnzt9WHMOOcajpUFOajz2VrN/Paui0Fea76NBgYkvplT3RGxP+OiCciMnPEktoBDb5LkTEVeBq4ELgxItbv8Rw9gOuB85LSVcA3IuJKMoFxLrCJD3ZjOiU9p63V19f0iKiOiOrKysqG2jdL7aRhvfnN+SfSq1NF3p+rvI0Y2qflrpGyry2MtcBXkknODx2jmuxq3A68Wd+Cki6RdHZytxuZ+YmpkuYAwyXNkFRBZhfisohYkYztABwtqQw4AQiglg92L44FljeiZlYQxxzcjXunjGFgzw55fZ6hVZ1pW95yB3I1eBxGRNQBF0o6Ebg62aJQ8rMA+GpEbGtg8enAXZImAy8AY7K2TOZExGRJU4CRwOWSLgduBn4I/BIYAMwD7iQTak9K6gd8GjiRTJCkqZkVzICeHbl3yhjO+1UNi19fn5fnOLJfl7ysNy1FIY9CaSJJ3YGJwNyIWN2Y2r5UV1dHTU1N/hq3krRl+w4uuGMRTyxZk/N1/+BvjuScMQP3O05SbURU5/r59zvpKemYrNvKuv2lXDfTkIh4NyLuyg6BtDWzQutQUc70s0by5RxcVGlPR/Vv2S2MNJ+SXJd1+/Gs21Ny24rZgaO8rA0/PONoLjwld4cDSZnD01tSY08C3Dq+AWNWBCRx4SlD+dEZR+fkWI3DKjvRoaJlz0iR5tn7SPoKmbCoyr6d187MDhBfHnUolZ3bcuFvnmXbjqZ/ufuY/l1z2FXT7HfSU9I/N/RYRPwg5x0VkCc97UCVr0nP/W5hFHsomFnu+EJGZpaaA8OsmW6bt5wpt9cy989r2bWr9R/X1Bw+CbBZMyxbu4lpf3iZbXW7ePiF1fTv1p5Jxx/C31YfQp+u7Vq6vZzzFoZZE+3YuYvv3r2YbXUfXFJg1fqtXDvzz4z50eP8/S0LmPnSW+xo4iUHWiNvYZg10c/nLmNRAxcq2hXw+JI1PL5kDVVd2vKlkYcw6fhDOKRHfr+clm8ODLMmePnNjVw368+pxr618X1umP0qN855lbGH9+LLxx/KxI9WUVFefBv4DgyzRtq+YxffuWsxdTsbN8EZAU8ufZsnl75Nz44VTJlwGJPHDc5Tl/lRfBFn1sJ++vifefnNjc1axzubt/PYS2/lqKPCcWCYNcK2up3cOm/F/gemUIwfwTowzBqh3UFlXPHZj+ZkXTscGGYHvi+OPJjPHtO32evZVQQnr9qTA8OskSQx7fSj6d+t/f4H78NOb2GYlYau7Q/iJ5OG05zTXDgwStC2Ol+8vlSNGtSDC046vMnLOzBKzL21K/nYj57g0Rd9CtFS9e1PDGHEod2atOxOz2GUjhlPLuO7dy/mnc3b+fpttVz5Xy8V5cdk1jzlZW346aQRdG7X+GMgi/Hfi4/0bKSI4OpHXuFn//2XD9UffXE1//TZI1qoK2tJh/bswHP//Em2bN/J+q11rN+ynQ1b69iwpY4NW+tYvzX5c0sdG7fWsX7rdtZvqaNj2+L79Su+jlvQjp27+N79z3NXzcq9Hht9WE+yrsJgJUYSHduW07FtebM/PWnNHBgpbavbyQV3LGLWy/UfzjvmsJ4F7sis8DyHkdKPHl7SYFhAZgvD7EDnwEihbucufr/4jQYfH9izA327HriboWa7OTBSeHLpWtZt3t7g46MP61XAbsxajgMjhQcWNbx1Ad4dsdLhwNiPTe/v4LGXGj4wq6K8DaMHOzCsNDgw9uOxF1d/6CSv2ST49y8Pp7Jz2wJ3ZdYy8hYYknpImiipqHfwH3i24d2RKz9/FKce1fyvOZsVi7wEhqS+wEPAKGC2pMqkXiVpUXK7q6SHJc2UdL+kCklTJM1Jfp6V9HNJ5ZJey6ofnSz/A0kLJN2Q9bx71Zpj7Xvv89TStfU+9q2TD+esEwfk4mnMika+tjCOBC6KiGnAo8BxSf0aYPfnj2cC10bERGA1cGpE3BwREyJiAvAkMB04Brhzdz0inpdUDYwlE0grJZ1SX625L+LB596gvsP9J1UfwncmDm3u6s2KTl4CIyJmRcR8SePJ/ALPk3QysJlMOBARN0XEzGSRSmDN7uUl9QeqIqIWOBE4XdJTkn4tqRwYD9wbmUvPzwLGNVDbi6TzJdVIqlm7tv6th93q2x35xEd6M+30o3wYuJWkfM5hCJgE1AECrgAurWfcaKB7RMzPKk8Fbk5uLwA+HhFjgfXAaUBHYFXy+EagqoHaXiJiekRUR0R1ZWXlPl/DVacfxdSTDmNQr44AHHdoN274ynGUl3mu2EpT3r5LkvxPP1XSvwAXAjdGxPrs/5kl9QCuB/5XVq0NcFJEfC8pPRcR7ye3lwBDgE18sGvTiUzw1VdrliP7deXIfl25+JPDePnN9+jbtR3tK8qau1qzopWvSc9LJJ2d3O0GnEomPOYAwyXNkFQB3AVcFhHZ520fBzyTdf82ScdKKgNOBxYDtWTmKwCOBZY3UMvV6+Gj/brQvWNFrlZpVpTytW09HThL0lygDBiTNZn5bERMBv4eGAlcnnz6MSlZ9lPA3Kx1XQncBjwLzIuIWcBTwAhJPyWzm3NnAzUzyyFFEZ4mDEBSe+AzwMKIWNZQbV+qq6ujpqYmv42atQBJtRFRnev1Fu35MCJiK3DP/mpmljue7jez1BwYZpaaA8PMUnNgmFlqDgwzS82BYWapOTDMLDUHhpml5sAws9QcGGaWmgPDzFJzYJhZag4MM0vNgWFmqTkwzCw1B4aZpebAMLPUHBhmlpoDw8xSc2CYWWoODDNLzYFhZqk5MMwsNQeGmaXmwDCz1BwYZpaaA8PMUnNgmFlqDgwzSy1vgSGph6SJknrl6znMrLDyEhiS+gIPAaOA2ZIqk3qVpEXJ7a6SHpY0U9L9kiokTZE0J/l5VtLPk7G/kPS0pO9nPUeqmpnlTr62MI4ELoqIacCjwHFJ/RqgfXL7TODaiJgIrAZOjYibI2JCREwAngSmSzoDKIuIMUA/SUPS1vL02sxKVnk+VhoRswAkjSezlXGlpJOBzWTCgYi4KWuRSmDN7juS+gNVEVEr6RzgruShJ4CxwIiUtaU5f3FmJSyfcxgCJgF1gIArgEvrGTca6B4R87PKU4Gbk9sdgVXJ7Y1AVSNq9fV1vqQaSTVr165t2oszK1F5C4zImAo8DVwI3BgR67PHSOoBXA+cl1VrA5wUEbOT0iY+2I3plPSctlZfX9MjojoiqisrK5vzEs1KTr4mPS+RdHZytxtwKjBV0hxguKQZkirI7EJcFhErshYfBzyTdb+WzO4FwLHA8kbUzCyH8jKHAUwH7pI0GXgBGBMRASBpTkRMljQFGAlcLuly4OaI+C3wKWBu1roeAJ6U1A/4NHAiEClrZpZDSn6PWzVJ3YGJwNyIWN2Y2r5UV1dHTU1N/ho3ayGSaiOiOtfrzdcWRk5FxLt88AlIo2pmljs+NNzMUnNgmFlqDgwzS82BYWapOTDMLDUHhpml5sAws9QcGGZ7eGvjNq5+ZAkPLFq1/8ElpigO3DIrpPnL3uHmOX+hrI3o0r6ckz9S7xefS5K3MMz2MLSqMwA7dwXf/PVCapava+GOWg8HhtkeBld2pKyNANhWt4vzblnAktUbW7ir3EnOtzs2uf01SZcmtydK+ui+lnVgmO2hbXkZg3t1ZGhVJwA2btvBNY/+uYW7yrkZkg4H3gV2SOoD/BjYsK+FPIdhVo9ff+0EKju1Zf6ydcx86S2++8mhLd1SzkTEOklfA7pmlfsB34yIfc70OjDM6tG7czsARh/Wk9GH9WzhbnJH0tfJnIB7V1KqBNoBn00eLwduj4if1be8A8OstNwCzIiInZIuAk4jc1a830XETyS1JXMO3no5MMxKSES8DyBpAHAumXmLSuBkSUsj4sF9Le9JT7MSI2kEmQuNXUzm0h8A5wD/JOlWSUc2tKwDw6yEJGFwD3BBRDy2ux4R64DxwKtktjjq5V0SsxISES9K+khE1CWlCpIcSHZXrtzX8g4MsxKTFRZExB2NWda7JGaWmgPDzFJzYJhZakVxIaN8kbQWWLGfYb2AtwvQTlO05t6gdfd3oPc2ICJyfvHgkg6MNCTV5OMKUrnQmnuD1t2fe2sa75KYWWoODDNLzYGxf9NbuoF9aM29Qevuz701gecwzCw1b2GYWWoOjCzJuQ4nSurV0r3Up7X3Zwe+kgoMSTdJ+pykKZLmJD/PSvq5pL5kvvI7CpgtqTJZ5heSnpb0/az1pKrluz9J5ZJeyxp7dLKeH0haIOmGrHXvVctVb1ljqiQt2td7ko/3rrG9FfJ9219/ze0lF/01SkSUxA8wDrivnvr1wEjgFODEpHYN8CngDOCWpHYTMCRtrUD9HQdcvcf4auBxMmdNujRZbq9aLnvLun8bsCS5XZD3rom9FeR9S/n32uRectFfY39KYgtD0kHAfwDLJX0+q94fqIqI2oiYFRHzJY0n87/4PGACcFcy/AlgbCNqhejvROB0SU9J+rUy52McD9wbmX95s8j8g62vlrPekvsnkzkZy+pkSH3vSdpavnvL+/vWiP6a00uz+muKkggM4GzgJeBfgVGSvpXUpwI37x4kScAkoA7YCXQEdp9FeSNQ1YhaIfpbAHw8IsYC68mcnzHX/e23N0kVwBVk/pfbrRDvXVN7K8T7lqq/ZvbS3P4arVQCYwQwPSJWA7cDJ0lqA5wUEbN3D4qMqcDTZM6ivAlonzzcicz7lbZWiP6ei4g3k4eXkNnEz3V/aXq7FLgxItZnLVeI966pvRXifUvbX3N6aW5/jVYqgfEqMDi5XU3mC2fjgGd2D5B0iaSzk7vdyKR9LR9sIh8LLG9ErRD93SbpWEllwOnA4jz0t9/eyOxPT5U0BxguaUYj+miJ3grxvqXtrzm9NLe/xsv3JElr+AE6A3cDc8ns+/cHrgLOyBrTHZiZjLmJzERSFzJ/gdcCL5O58EuqWoH6Owp4DngemJaMawP8Efgp8AowqL5aLnvbY/yc5M+8v3fN6C3v71sj/l6b3Etz+2vKj4/03A9J3YGJwNzIbFqmrrUUSe2BzwALI2JZQ7UC9FFU711red8a00uh+3NgmFlqpTKHYWY54MCwvJJUkRx3UN9jg+urW+vlwLB8+zfgkAYem5IciGZFwoFhzSbp/0p6Oev7EMOT+mhgTUTMb2DRy4B/SI5NsCLgCxlZrkyLiNv3qJ3Nh4+w/JCI2CFpFvAx4Ml8Nme54WS3fGofERsAJJ0p6ceSOklaKKlzMmY+mSMirQg4MCxXLs/aJSnb88GI+DVwJJmDjK6OiPeSh7byweHN1so5MCxXpkXEhORnZ1Jrk3xhbrcZZL4Dc3dWbRDweqGatOZxYFg+LSDzFWySr21/m8x5KaZmjfkC8FjBO7MmcWBYrmTvkkxKajPIfArSAbgQeJDMJyNflXSwpLHA2ohorVcgsz340HDLK0nHAH0j4tF6HvsOcH1E1BW+M2sKB4aZpeZdEjNLzYFhZqk5MMwsNQeGmaXmwDCz1BwYZpba/wdOKbSv0TTCAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "ax.ticklabel_format(useOffset=False, style='plain')\n",
    "# ax.ticklabel_format(axis='y', style='scientific', scilimits=(0,0))\n",
    "shp_file.plot(ax=ax)\n",
    "plt.text(x = 672900,y=3429500,s='米')\n",
    "plt.text(x = 675280,y=3426700,s='米')\n",
    "plt.xlabel('E(x)')\n",
    "plt.ylabel('E(y)')\n",
    "plt.savefig(r'E:\\python林业资源\\出图/缓冲前.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:32649>\n",
       "Name: WGS 84 / UTM zone 49N\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (metre)\n",
       "- N[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- name: Between 108°E and 114°E, northern hemisphere between equator and 84°N, onshore and offshore. China. Hong Kong. Indonesia. Macao. Malaysia - East Malaysia - Sarawak. Mongolia. Russian Federation. Vietnam.\n",
       "- bounds: (108.0, 0.0, 114.0, 84.0)\n",
       "Coordinate Operation:\n",
       "- name: UTM zone 49N\n",
       "- method: Transverse Mercator\n",
       "Datum: World Geodetic System 1984\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp_file.crs#查看shp文件投影信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>class</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((8486482.760 11412644.681, 8486631.15...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((8486412.849 11413001.954, 8486378.91...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((8487441.133 11413695.469, 8487488.75...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((8485367.869 11413409.083, 8485396.68...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((8485566.720 11410108.706, 8485691.24...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((8488477.314 11410762.874, 8488508.54...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((8489362.812 11413904.086, 8489438.36...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((8486755.334 11413918.645, 8487003.09...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  class                                           geometry\n",
       "0   0      5  POLYGON ((8486482.760 11412644.681, 8486631.15...\n",
       "1   0      5  POLYGON ((8486412.849 11413001.954, 8486378.91...\n",
       "2   0      5  POLYGON ((8487441.133 11413695.469, 8487488.75...\n",
       "3   0      5  POLYGON ((8485367.869 11413409.083, 8485396.68...\n",
       "4   0      5  POLYGON ((8485566.720 11410108.706, 8485691.24...\n",
       "5   0      5  POLYGON ((8488477.314 11410762.874, 8488508.54...\n",
       "6   0      5  POLYGON ((8489362.812 11413904.086, 8489438.36...\n",
       "7   0      5  POLYGON ((8486755.334 11413918.645, 8487003.09..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp_file.to_crs(\"EPSG:32633\")#修改shp文件投影信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:32649>\n",
       "Name: WGS 84 / UTM zone 49N\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (metre)\n",
       "- N[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- name: Between 108°E and 114°E, northern hemisphere between equator and 84°N, onshore and offshore. China. Hong Kong. Indonesia. Macao. Malaysia - East Malaysia - Sarawak. Mongolia. Russian Federation. Vietnam.\n",
       "- bounds: (108.0, 0.0, 114.0, 84.0)\n",
       "Coordinate Operation:\n",
       "- name: UTM zone 49N\n",
       "- method: Transverse Mercator\n",
       "Datum: World Geodetic System 1984\n",
       "- Ellipsoid: WGS 84\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp_file.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "g1 = shp_file.buffer(0.5,resolution = 16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x234e3697a20>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e36c1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    POINT (674339.068 3428330.892)\n",
       "1    POINT (674585.822 3428369.220)\n",
       "2    POINT (674816.671 3427737.890)\n",
       "3    POINT (674882.675 3428823.444)\n",
       "4    POINT (673143.032 3429182.746)\n",
       "5    POINT (673068.698 3427578.404)\n",
       "6    POINT (674624.299 3426793.403)\n",
       "7    POINT (674911.936 3428016.326)\n",
       "dtype: geometry"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mid_point = shp_file.centroid#查看每个面的中心点\n",
    "mid_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rNuzg+v9Zwf1fH5ax5wvw8DtXTWWVsWT1FmYvW89zb2xM+CvBuiz42yf85Lm3ueWcE5PcYXJ4+J0LvPfJ58xetoE/rNhQ76sAJfLAotX07tSOy4b3TMr0ksnD7yJty859PPP6R8xZtoFVG3akZB63Pf0GPQrbcnotJxxNBw+/i5zKKgu249ez8J3NHEjxhTsOVBmTZ5Tz1LdG069z3ecxaCp+kI+LnCzBh1t38+e3NqU8+Ad9vvcAVz78Glt27muS+YURKvySiiSNl5RZ6y3ONYAkrv3icfzi0iHkZjfdnvgPt+7hmkfKM+YiIXWGX1Ih8CwwHHhBUnFQL5G0PLidL2mepAWSnpKUJ6lQ0lxJZZIeiJveQ5IWS/p+fWvOJdOFQ7rx8JXDad+q6bZ+y9du48ZZK7Gw3xmmUJgl/yDgBjO7C5gPnBLU7wHaBLcnAfea2QRgIzARuByYYWalwDGSSiVdDGSb2Sigr6T+YWtJer7OHeG0fp144tqRdD6mVYPGD3NNwOqeef0j/uvP7zVofslUZ/jN7EUzWyJpDLGl/2JJ44BdxIKOmU01s+eDUYqBTcAWYKCkAqAH8CEwFpgZDLcAOL0eNedS4qSu+cz51miOK67/acPCXhOwul/85T2eXr6h7gFTKOw2v4BLgG2AgNuAm2sYbhRQaGZLgJeBXsB3gLeArUA74OAz3gqU1KNWfV7XBJsUZZs3bw7zNJxLqHthW2ZPHs2wXoX1Gi/sNQFr8t8vvF/vcZIpVPgtZgqwErgemGpmR1xxQVIRcB9wVVC6HfgXM7sTeBu4EtjJ4U2F9sH8w9aq9zTdzErNrLS4uO7TOztXl4K2ecy4egQTBoS/ClDYawLW5MRj0/vLvzA7/G6SdEVwt4DY9vwUSQuBIZIelJQHPAncYmZrg2ELgZMlZQMjAAPKObwKPxhYU4+acynXOjebaV8fxuUje6V8XgPT/LPfMLs5pwMzJV0NvAGMtmBXpaSFZna1pMnEdgTeKulWYBrwn8Bvia36LwYeJ/Zh85KkrsDZwEhiHwphas41iewsceeFJ9ElvzX/f/47KZvPwG75KZt2GGrqrxyCrw7HA4vMbGN9aomUlpZaWVlZaht3kTS7fD03zV6ZkoOBVvzHeAra1n62YknlwTdmSdfkh/ea2TYO78mvV825pvaVYd0pPqYVkx8tT3h1oYboXtimzuCnmh/e61wdxhxfzBPXjqJT+4YdC1CTgV3Tu8oPHn7nQhnYLZ+nvjWavo28hNhBJ2XAOf48/M6F1KOoLbMmj2Zoz4JGTyvdO/vAf9LrXL0Utcvj8X8eyYdbdzdqOj2K2iapo4bz8DtXT61zs+lfkjm/y28oX+13LqI8/M4FzIx75r/D3FUfs/9AVbrbSTlf7XcuMHvZhkM/tunYLo+vDuvOJaf2oG9x+zR3lhoefueAj7bv4Y5n3jx0f8uu/TywaDUPLFrNiD5FXDa8JxMHdqF1bnYau0wuD7+LvKoq48ZZK/l834EaH3/1g628+sFW8p/J5ctDu3HZ8J6c0KX57/Dz8LvIe/TVtbz8/qd1DrdjTwUPv7KGh19Zw9CeBVx6ag/OG9SVdk14GrBkap5dO5ckH3y6i/+c+3a9x1u+bjvL123nh8++xfmDu3LduH50K2hT94gZxPf2u8iqrDK+9+Tr7GnE2XR37jvA40vXsfSDLUnsrGl4+F1k/fH1jyhfuy0p0zpQmf6z8daXh99F1vgBJfQJ8UOdMGforcqAU3HXl4ffRVa7Vjn81yVDyKnjEtphztBb2QyPCfLwu0gb3KOA7044odZhwpyht7IZLvl9b7+LvGvH9GXRu5tZvLrmnXa3T5hc59l5K5vhot+X/C7ysrLEvZcMpqBtboOn0Qz393n4IfaDjrvnvc1//OEN9h3IjIsouqZ1bH4b7r54UIPHr2qiq/0mU+TDf6Cyiptmr+T+F//O7xev5SvTXmHtll3pbsulwcSBXbhseM8GjdtUl/pOpkhv8++tqOS6x5bz57c+OVR7Y8NnPLZ0HbecfWIaO3Ppcvv5A5g0oic79lSwfXdF7N89+9lx8PahWgWf7alg++797Npf2Sy/6ots+HfsqeCff1fG0jVbj3ps9HGd0tCRywStc7PrfX69/QeqPPzNxabP9nLFb5by9sbPj3osJ0uU1vNijS7a8nKa59Zz5MJfWWVc+uslrN5c83b9kB4FzfZXWs7VR/P8yGqEJau3JAw+wKjjOjZhN86lT+TC//TyDYdu13TMtoffRUWkwr+3opLn3jh8zc/qx2zn5WRxSk/f3nfREKnw/+/bm444VVP1Y7aH9SxsUedoc642kdqzFb/KD0ces53fJpcfXnRSOtpyLi1CLfklFUkaL6nZfgG+ffd+Fr6zucbHWuVk8ZtvlNKvc/M/KaNzYdUZfkmFwLPAcOAFScVBvUTS8uB2vqR5khZIekpSnqTJkhYGfyskPSApR9K6uPrJwfh3SHpN0q/i5ntUrTHmrtrI/hp+eZUl+O9/OoVhvYqSMRvnmo0wS/5BwA1mdhcwHzglqN8DHDxj4STgXjObAGwEJprZNDMba2ZjgZeAXwfTevxg3cxWSRoGnE7sw2WTpLNqqjX2iT69YkON9R9/+WTGDyhp7OSda3bqDL+ZvWhmSySNIRbGxZLGAbuIBR0zm2pmzwejFAObDo4vqRtQYmZlwEjgPElLJT0kKQf4IjDbzIzYh8sZCWoNtmH7HpZ+cPRhvDeMP55LG/hDDueau7Db/AIuAbYBAm4Dbq5huFFAoZktiStPAQ5+kf4acJaZDQdygXOAdsDBxfJWoCRBrfq8rpFUJqls8+aat+UPys0W3/mH/vTrfPiyS5NG9OTb4/rVOp5zLVmovf3BEniKpB8C1wNTzWx77DMhRlIRcB/wlbhaFnAmcGtQWmlm+4LbZUB/YCeHNx/aE/tAqqlWvafpwHSA0tLSWn9V0fmY1tww/nhuGH88737yOa+u3sI/jehFfP/ORU2YHX43SboiuFsATCT2QbAQGCLpQUl5wJPALWa2Nm70M4BXgw8PgEckDZaUDVwEvA6UE9u+BxgMrElQS4rjS47h8lG9ya7jpI3OtXRhVvunA5dLWgRkA6PjduStMLOrgW8S2xF4a7AX/5Jg3C8Bi+KmdSfwCLACWGxmfwZeBoZK+gWxTYnHE9Scc0kky4DfIUtqA5wLLDOz1YlqiZSWllpZWVnqG3WuiUkqN7PSVEw7I47wM7M9wKy6as655InUsf3OucM8/M5FlIffuYjy8DsXUR5+5yLKw+9cRHn4nYsoD79zEeXhdy6iPPzORZSH37mI8vA7F1EefuciysPvXER5+J2LKA+/cxHl4Xcuojz8zkWUh9+5iPLwOxdRHn7nIsrD71xEefidiygPv3MR5eF3LqI8/M5FlIffuYjy8DsXUR5+5yIqVPglFUkaL6lTqhtyzjWNOsMvqRB4FhgOvCCpOKiXSFoe3M6XNE/SAklPScqTNFnSwuBvhaQHgmEfkrRY0vfj5hGq5pxLnjBL/kHADWZ2FzAfOCWo3wO0CW5PAu41swnARmCimU0zs7FmNhZ4Cfi1pIuBbDMbBfSV1D9sLUnP1zkXyKlrADN7EUDSGGJL/zsljQN2EQs6ZjY1bpRiYNPBO5K6ASVmVibpCmBm8NAC4HRgaMjaew14fs65BMJu8wu4BNgGCLgNuLmG4UYBhWa2JK48BZgW3G4HbAhubwVK6lGrPq9rJJVJKtu8eXOYp+GcixMq/BYzBVgJXA9MNbPt8cNIKgLuA66Kq2UBZwILg9JODm8qtA/mH7ZWvafpZlZqZqXFxcVhnoZzLk6YHX43BavrAAXARGCKpIXAEEkPSsoDngRuMbO1caOfAbxqZhbcLye2Cg8wGFhTj5pzLonq3OYHpgMzJV0NvAGMPhhmSQvN7GpJk4ntCLxV0q3ANDN7AvgSsChuWk8DL0nqCpwNjAQsZM05l0Q6vFBuohnGvjocDywys431qSVSWlpqZWVlqW3cuTSQVG5mpamYdpglf1KZ2TYO78mvV805lzx+eK9zEeXhdy6iPPzORZSH37mI8vA7F1EefuciysPvXER5+F2L9slne3nwpdXsrahMdysZp8kP8nGuKU2ZsYyytdtYsnoL9399GDnZvrw7yF8J16L1L2nPHQumcf+VI1lyzmVUVTXt4eyZzMPvWrT+nY9h0op55FgVI5+fxd3PvZ3ulpIqOL/m6cHtayTdFNw+R9KA2sb18LsW7fiSY/ifoWdzQFnMGHI20xetZv6btf5OrDl6UFI/Yie+qZTUG/gFsKO2kXyb37VoA7t1oGTu46yRWLnw71zZJofxJx51Yqhmy8y2SroGyI8rdwSuNLMNCUYDPPyuhStom0dB2zwAfvaPg9PcTXJJupbYyXOrglIx0Bo4L3g8B3jUzO6vaXwPv3PN18PAg2ZWKelfgXOInW3rD2b2c0mtiJ1zs0YefueaKTPbByCpF3Al8DNiS/8zJb1nZs/WNr7v8HOuGZM0FPgT8D1ip9MH+AZwm6TfSzop0bgefueaqSDYs4DrzGzBwbqZbQXGAO8TWxOoka/2O9dMmdmbkr5gZhVBKY8g08EmwZ21je/hd64Ziws+ZvZYfcb11X7nIsrD71xEefidi6gmv2hHKkjaDKytY7BOwKdN0E5DZXJ/mdwbZHZ/je2tl5ml5GKULSL8YUgqS9WVT5Ihk/vL5N4gs/vL5N58td+5iPLwOxdRUQr/9HQ3UIdM7i+Te4PM7i9je4vMNr9z7khRWvI75+K02PAH5zYbL6lTunupLpN7c9HRbMMvaaqk8yVNlrQw+Fsh6QFJhcCzwHDgBUnFwTgPSVos6ftx0wlVS2VvknIkrYsb9uRgOndIek3Sr+KmfVQtmf3FDVMiaXltr0lTv3Y19daUr10d72uj+kjG+1pvZtbs/oAzgDk11O8DSoEvAiOD2j3Al4CLgYeD2m+A/mFrTdDbKcBPqg0/DPgLsTOx3A6cVVMt2a9d3P1HgLeD2xnx2iXorUleuxDva4P7SMb72pC/Zrfkl5QL/BpYI+nCuHo3oMTMyszsRTNbImkMsSXsYmAsMDMYfAFwej1qqe5tJHCepKXBkjOH2IfEbIv9L5pP7D9fTbXQwvQX3B9H7MQQB09zW9NrEraW6t5S/tqF7K0xfTTqfW2oZhd+4Argb8BPgeGSvh3UpwDTDg4kScAlwDagAmgHHDyb6VagpB61VPf2GrFP++FALrFzsSW7t1D9ScoDbgNujhsvI167BL01xWsX5n1tTB+NfV8bpDmGfygw3cw2Ao8SO19ZFnAmsPDgQBYzBVgJXADsBNoED7cn9tzD1lLd20oz+zh4uIzYKnSyewvb383AVDPbHjdeprx2NfXWFK9dmN4a00dj39cGaY7hfx/oG9wuJfaDnjOAV4PVJiTdJOmKYJgCYDtQzuHV0MHAmnrUUt3bI5IGS8oGLgJeT0Fvofojtg06RdJCYIikB+vRS0pfuwS9NcVrF6a3xvTR2Pe1YZpix0Iy/4BjgCeBRcS2l7sBPwYujhumEHg+GGYqsR0pHYi9IfcCbxG7yEGoWhP0NpDYWsAq4K5guCzgr8SuvPIO0KemWrJfu2rDLwz+zYjXLkFvKX/tQr6vDe6jse9rQ/8idYSfYl+zjQcWWWwVLnQtTf22Ac4FlpnZ6kS1JurFX7sU9pGW3qIUfufcYc1xm985lwQefuciysPvXER5+J2LKA+/cxH1f0rdIx30BeG8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e6848668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ax1 = shp_file.plot()\n",
    "# ax2 = mid_point.plot(ax = ax1,color = 'r',markersize = 4.0)\n",
    "fig,ax = plt.subplots()\n",
    "ax.ticklabel_format(useOffset = False,style='plain')\n",
    "# ax.ticklabel_format(axis='y', style='scientific', scilimits=(0,0))\n",
    "ax1 = shp_file.plot(ax=ax)\n",
    "ax2 = mid_point.plot(ax = ax1,color = 'r',markersize = 4.0)\n",
    "plt.text(x = 672900,y=3429500,s='米')\n",
    "plt.text(x = 675280,y=3426700,s='米')\n",
    "plt.xlabel('E(x)')\n",
    "plt.ylabel('E(y)')\n",
    "plt.savefig(r'E:\\python林业资源\\出图/缓冲前.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "g3 = shp_file.boundary#降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x234e6855128>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234db59c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g3.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "g4 = shp_file.convex_hull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x234e69097f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e68c6320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g4.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21280.687722\n",
       "1    34218.706431\n",
       "2    34003.293813\n",
       "3    44261.020422\n",
       "4    18046.631075\n",
       "5    16140.265255\n",
       "6     2632.263329\n",
       "7    76240.071834\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp_file.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "g2 = shp_file.buffer(30,resolution=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e6956438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ax = g2.plot()\n",
    "# ax.axis('off')\n",
    "fig,ax = plt.subplots()\n",
    "ax.ticklabel_format(useOffset = False)\n",
    "# ax.ticklabel_format(axis='y', style='scientific', scilimits=(0,0))\n",
    "ax1 = g2.plot(ax=ax)\n",
    "plt.text(x = 672900,y=3429500,s='米')\n",
    "plt.text(x = 675280,y=3426700,s='米')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     42421.597133\n",
       "1     57879.765552\n",
       "2     58549.330535\n",
       "3     71302.410483\n",
       "4     36800.467612\n",
       "5     35325.231473\n",
       "6     12065.455256\n",
       "7    112797.868530\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g2.area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     610.603061\n",
       "1     694.605684\n",
       "2     724.104931\n",
       "3     807.288199\n",
       "4     531.036567\n",
       "5     545.402474\n",
       "6     221.856301\n",
       "7    1124.501779\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp_file.length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.55, 10.55, -0.5, 10.5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e68c6978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "polygon1 = gpd.GeoDataFrame({\n",
    "    'value1': [1, 2],\n",
    "    'geometry': [shapely.geometry.Polygon([(1, 0), (3, 0), (3, 10), (1, 10)]),\n",
    "                 shapely.geometry.Polygon([(6, 0), (8, 0), (8, 10), (6, 10)])]\n",
    "})\n",
    "\n",
    "polygon2 = gpd.GeoDataFrame({\n",
    "    'value2': [3, 4],\n",
    "    'geometry': [shapely.geometry.Polygon([(-1, 3), (-1, 5), (10, 5), (10, 3)]),\n",
    "                 shapely.geometry.Polygon([(-1, 6), (-1, 8), (10, 8), (10, 6)])]\n",
    "})\n",
    "\n",
    "ax = polygon1.plot(color='red', alpha=0.4)\n",
    "ax = polygon2.plot(color='grey', alpha=0.4, ax=ax)\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6499999999999999, 8.35, 2.7499999999999996, 8.25)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e693f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g5 = gpd.overlay(polygon1,polygon2,how='intersection')\n",
    "ax = g5.plot()\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.5499999999999998, 10.549999999999999, -0.5, 10.5)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e6a744a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g6 = gpd.overlay(polygon1,polygon2,how='union')\n",
    "ax = g6.plot()\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.5499999999999998, 10.549999999999999, -0.5, 10.5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e6ae6b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g7 = gpd.overlay(polygon1,polygon2,how='symmetric_difference')\n",
    "ax = g7.plot()\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6499999999999999, 8.35, -0.5, 10.5)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e69aa358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g8 = gpd.overlay(polygon1,polygon2,how='difference')\n",
    "ax = g8.plot()\n",
    "ax.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6499999999999999, 8.35, -0.5, 10.5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x234e67cf128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g9 = gpd.overlay(polygon1,polygon2,how='identity')\n",
    "ax = g9.plot()\n",
    "ax.axis('off')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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